# Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks

arXiv: https://arxiv.org/abs/1905.05137 · HTML: https://mlsanity.com/paper/1905.05137/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2019.

## Built on by (most cited first, 16 of 16)

- [Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2203.09702/) (2022)
- [SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection](https://mlsanity.com/paper/2308.06819/) (2023)
- [Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT](https://mlsanity.com/paper/2104.12426/) (2021)
- [Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems](https://mlsanity.com/paper/2405.18624/) (2024)
- [Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review](https://mlsanity.com/paper/2112.03315/) (2021)
- [Adversarial Training for Deep Learning-based Intrusion Detection Systems](https://mlsanity.com/paper/2104.09852/) (2021)
- [Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey](https://mlsanity.com/paper/2312.06718/) (2023)
- [Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples](https://mlsanity.com/paper/2207.01531/) (2022)
- [Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems](https://mlsanity.com/paper/2303.07003/) (2023)
- [DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning](https://mlsanity.com/paper/2101.03218/) (2021)
- [Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Ones](https://mlsanity.com/paper/2306.05494/) (2023)
- [NetDiffuser: Deceiving DNN-Based Network Attack Detection Systems with Diffusion-Generated Adversarial Traffic](https://mlsanity.com/paper/2603.08901/) (2026)
- [Hack The Box: Fooling Deep Learning Abstraction-Based Monitors](https://mlsanity.com/paper/2107.04764/) (2021)
- [AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networks](https://mlsanity.com/paper/2203.09792/) (2022)
- [Uncovering and Understanding FPR Manipulation Attack in Industrial IoT Networks](https://mlsanity.com/paper/2601.14505/) (2026)
- [PLAA: Packet-level Adversarial Attacks in Network Traffic Detection](https://mlsanity.com/paper/2606.28439/) (2026)

## Abstract (arXiv)

Adversarial attacks have been widely studied in the field of computer vision but their impact on network security applications remains an area of open research. As IoT, 5G and AI continue to converge to realize the promise of the fourth industrial revolution (Industry 4.0), security incidents and events on IoT networks have increased. Deep learning techniques are being applied to detect and mitigate many of such security threats against IoT networks. Feedforward Neural Networks (FNN) have been widely used for classifying intrusion attacks in IoT networks. In this paper, we consider a variant of the FNN known as the Self-normalizing Neural Network (SNN) and compare its performance with the FNN for classifying intrusion attacks in an IoT network. Our analysis is performed using the BoT-IoT dataset from the Cyber Range Lab of the center of UNSW Canberra Cyber. In our experimental results, the FNN outperforms the SNN for intrusion detection in IoT networks based on multiple performance metrics such as accuracy, precision, and recall as well as multi-classification metrics such as Cohen's Kappa score. However, when tested for adversarial robustness, the SNN demonstrates better resilience against the adversarial samples from the IoT dataset, presenting a promising future in the quest for safer and more secure deep learning in IoT networks.